Method and apparatus for generating a customer risk assessment using dynamic customer data

ABSTRACT

A computer implemented method, apparatus, and computer usable program product for generating a customer risk assessment score. In one embodiment, the process parses dynamic data associated with a customer to identify patterns of events. The dynamic data comprises metadata describing an appearance and behavior of the customer. The patterns of events are analyzed to identify risk assessment factors for the customer. A risk assessment analysis is dynamically performed using the risk assessment factors for the customer to generate a risk assessment score for the customer while the customer is shopping in a retail facility. The risk assessment score indicates a potential risk posed by the customer to the retail facility.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of patent application U.S.Ser. No. 11/695,983, filed Apr. 3, 2007, titled “Method and Apparatusfor Providing Customized Digital Media Marketing Content Directly to aCustomer”, which is incorporated herein by reference.

The present invention is also related to the following applicationsentitled Identifying Significant Groupings of Customers for Use inCustomizing Digital Media Marketing Content Provided Directly to aCustomer, Application Ser. No. 11/744,024, filed May 3, 2007; GeneratingCustomized Marketing Messages at a Customer Level Using Current EventsData, application Ser. No. 11/769,409, file Jun. 24, 2007; GeneratingCustomized Marketing Messages Using Automatically Generated CustomerIdentification Data, application Serial No. 11/756,198, filed May 31,2007; Generating Customized Marketing Messages for a Customer UsingDynamic Customer Behavior Data, application Ser. No. 11/771,252, filedJun. 29, 2007, Retail Store Method and System, Robyn Schwartz,Publication No. US 2006/0032915 A1 (filed Aug. 12, 2004); BusinessOffering Content Delivery, Robyn R. Levine, Publication No. US2002/0111852 (filed Jan. 16, 2001) all assigned to a common assignee,and all of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related generally to an improved dataprocessing system, and in particular to a method and apparatus forprocessing digital video data. More particularly, the present inventionis directed to a computer implemented method, apparatus, and computerusable program product for processing digital video data associated witha customer to generate a risk assessment score for the customer.

2. Description of the Related Art

In the past, merchants frequently had a personal relationship with theircustomers. The merchant often knew their customers' names, address,marital status, ages of their children, hobbies, place of employment,character, anniversaries, birthdays, likes, dislikes and personalpreferences. The merchant was able to use this information to cater tocustomer needs and push sales of items the customer might be likely topurchase based on the customer's personal situation. The merchant wasalso able to determine whether a customer was a good customer thatshould receive special marketing efforts, a credit risk, a bad customerthat should not receive special marketing offers, or a customer thatposed a risk or threat to the store or other customers based on themerchant's personal knowledge of the customer's character, reputation,and criminal history.

However, with the continued growth of large cities, the correspondingdisappearance of small, rural towns, and the increasing number of large,impersonal chain stores with multiple employees, the merchants andemployees of retail businesses rarely recognize regular customers, andalmost never know the customer's name or any other details regardingtheir customer's personal preferences that might assist the merchant oremployee in marketing efforts directed toward a particular customer.

One solution to this problem is directed toward using data miningtechniques to gather customer profile data. The customer profile data isused to generate marketing strategies for marketing products tocustomers. Customer profile data typically includes information providedby the customer in response to a questionnaire or survey, such as thename, address, telephone number, and gender of customers, as well asproducts preferred by the customer. Demographic data regarding acustomer's age, sex, income, career, interests, hobbies, and consumerpreferences may also be included in customer profile data.

However, these methods only provide limited and generalized marketingstrategies that are directed towards a fairly large segment of thepopulation without taking into account actual customer reactions toproduct placement in a particular retail store or to other environmentalfactors that may influence product purchases by customers.

In an attempt to better monitor customers in large retail stores, thesestores frequently utilize cameras and other audio and/or videomonitoring devices to record customers inside the retail store or in theparking lot. A store detective may watch one or more monitors displayingclosed circuit images of customers in various areas inside the store toidentify shoplifters. However, these solutions require a human user toreview the audio and video recordings. In addition, the video and audiorecordings are typically used only for store security.

Thus, current solutions do not utilize all of the potential dynamiccustomer data elements that may be available for identifying customersthat should be marketed to, customers that should be encouraged to shopat the retail facility, customers that should not receive marketingcontent, and customers that should be discouraged from shopping at theretail facility. The data elements currently being utilized to generatemarketing strategies only provide approximately seventy-five percent(75%) of the needed customer data.

SUMMARY OF THE INVENTION

The illustrative embodiments provide a computer implemented method,apparatus, and computer usable program product for generating a customerrisk assessment score. In one embodiment, the process parses dynamicdata associated with a customer to identify patterns of events. Thedynamic data comprises metadata describing an appearance and behavior ofthe customer. The process analyzes the patterns of events to identifyrisk assessment factors for the customer. The process dynamicallyperforms a risk assessment analysis using the risk assessment factorsfor the customer to generate a risk assessment score for the customer inreal-time as the customer is shopping in a retail facility. The riskassessment score indicates a potential risk posed by the customer to theretail facility.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a digital customer marketing environment inwhich illustrative embodiments may be implemented;

FIG. 3 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 4 is a block diagram of a data processing system for analyzingdynamic customer data in accordance with an illustrative embodiment;

FIG. 5 is a block diagram of a dynamic marketing message assemblytransmitting a customized marketing message to a set of display devicesin accordance with an illustrative embodiment;

FIG. 6 is a block diagram of an identification tag reader foridentifying items selected by a customer in accordance with anillustrative embodiment;

FIG. 7 is a block diagram illustrating an external marketing manager forgenerating current events data in accordance with an illustrativeembodiment;

FIG. 8 is a block diagram illustrating a smart detection engine forgenerating customer identification data and selected item data inaccordance with an illustrative embodiment;

FIG. 9 is a block diagram of a shopping container in accordance with anillustrative embodiment;

FIG. 10 is a block diagram of a shelf in a retail facility in accordancewith an illustrative embodiment;

FIG. 11 is a block diagram illustrating a set of risk assessment factorsused to generate a risk assessment score for a customer in accordancewith an illustrative embodiment;

FIG. 12 is a block diagram illustrating a risk assessment engine forgenerating a risk assessment score for a customer in accordance with anillustrative embodiment;

FIG. 13 is a flowchart illustrating a process for monitoring for achange in biometric readings associated with a customer in accordancewith an illustrative embodiment;

FIG. 14 is a flowchart illustrating a process for generating dynamicdata for a customer in accordance with an illustrative embodiment;

FIG. 15 is a flowchart illustrating a process for identifying anundesirable customer in accordance with an illustrative embodiment;

FIG. 16 is a flowchart illustrating a process for generating a riskassessment score in accordance with an illustrative embodiment;

FIG. 17 is a flowchart illustrating a process for updating a riskassessment score in accordance with an illustrative embodiment;

FIG. 18 is a flowchart illustrating a process for preferred customermarketing in accordance with an illustrative embodiment;

FIG. 19 is a flowchart illustrating a process for marketingdisincentives in accordance with an illustrative embodiment; and

FIG. 20 is a flowchart illustrating a process for generating acustomized marketing message using dynamic data in accordance with anillustrative embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference now to the figures and in particular with reference toFIGS. 1-3, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-3 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

With reference now to the figures, FIG. 1 depicts a pictorialrepresentation of a network of data processing systems in whichillustrative embodiments may be implemented. Network data processingsystem 100 is a network of computers in which embodiments may beimplemented. Network data processing system 100 contains network 102,which is the medium used to provide communications links between variousdevices and computers connected together within network data processingsystem 100. Network 102 may include connections, such as wire, wirelesscommunication links, or fiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage area network (SAN) 108. Storage area network 108is a network connecting one or more data storage devices to one or moreservers, such as servers 104 and 106. A data storage device, mayinclude, but is not limited to, tape libraries, disk array controllers,tape drives, flash memory, a hard disk, and/or any other type of storagedevice for storing data. Storage area network 108 allows a computingdevice, such as client 110 to connect to a remote data storage deviceover a network for block level input/output.

In addition, clients 110 and 112 connect to network 102. These clients110 and 112 may be, for example, personal computers or networkcomputers. In the depicted example, server 104 provides data, such asboot files, operating system images, and applications to clients 110 and112. Clients 110 and 112 are clients to server 104 in this example.

Digital customer marketing environment 114 is a retail environment thatis connected to network 102. A customer may view, select order, and/orpurchase one or more items in digital customer marketing environment114. Digital customer marketing environment 114 may include one or morefacilities, buildings, or other structures for wholly or partiallycontaining items.

The items in digital customer marketing environment 114 may include, butare not limited to, consumables, comestibles, clothing, shoes, toys,cleaning products, household items, machines, any type of manufactureditems, entertainment and/or educational materials, as well as entranceor admittance to attend or receive an entertainment or educationalactivity or event. Items for purchase could also include services, suchas, without limitation, dry cleaning services, food delivery services,automobile repair services, vehicle detailing services, personalgrooming services, such as manicures and haircuts, cookingdemonstrations, or any other services.

Comestibles include solid, liquid, and/or semi-solid food and beverageitems. Comestibles may be, but are not limited to, meat products, dairyproducts, fruits, vegetables, bread, pasta, pre-prepared or ready-to-eatitems, as well as unprepared or uncooked food and/or beverage items. Forexample, a comestible includes, without limitation, a box of cereal, asteak, tea bags, a cup of tea that is ready to drink, popcorn, pizza,candy, or any other edible food or beverage items.

An entertainment or educational activity, event, or service may include,but is not limited to, a sporting event, a music concert, a seminar, aconvention, a movie, a ride, a game, a theatrical performance, and/orany other performance, show, or spectacle for entertainment or educationof customers. For example, entertainment or educational activity orevent could include, without limitation, the purchase of seating at afootball game, purchase of a ride on a roller coaster, purchase of amanicure, or purchase of admission to view a film.

Digital customer marketing environment 114 may also includes a parkingfacility for parking cars, trucks, motorcycles, bicycles, or othervehicles for conveying customers to and from digital customer marketingenvironment 114. A parking facility may include an open air parking lot,an underground parking garage, an above ground parking garage, anautomated parking garage, and/or any other area designated for parkingcustomer vehicles.

For example, digital customer marketing environment 114 may be, but isnot limited to, a grocery store, a retail store, a department store, anindoor mall, an outdoor mall, a combination of indoor and outdoor retailareas, a farmer's market, a convention center, a sports arena orstadium, an airport, a bus depot, a train station, a marina, a hotel,fair grounds, an amusement park, a water park, and/or a zoo.

Digital customer marketing environment 114 encompasses a range or areain which marketing messages may be transmitted to a digital displaydevice for presentation to a customer within digital customer marketingenvironment. Digital multimedia management software is used to manageand/or enable generation, management, transmission, and/or display ofmarketing messages within digital customer marketing environment.Examples of digital multimedia management software include, but are notlimited to, Scala® digital media/digital signage software, EK3® digitalmedia/digital signage software, and/or Allure digital media software.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as, without limitation, anintranet, an Ethernet, a local area network (LAN), and/or a wide areanetwork (WAN).

Network data processing system 100 may also include additional datastorage devices in addition to or instead of storage area network 108,such as, without limitation, one or more hard disks, compact disks (CD),compact disk rewritable (CD-RW), flash memory, compact disk read-onlymemory (CD ROM), non-volatile random access memory (NV-RAM), and/or anyother type of storage device for storing data.

FIG. 1 is intended as an example, and not as an architectural limitationfor different embodiments. Network data processing system 100 mayinclude additional servers, clients, data storage devices, and/or otherdevices not shown. For example, server 104 may also include devices notdepicted in FIG. 1, such as, without limitation, a local data storagedevice.

In another embodiment, digital customer marketing environment 114includes one or more servers located on-site at digital customermarketing environment. In this example, network 102 is optional. Inother words, if one or more servers and/or data processing systems arelocated at digital customer marketing environment 114, the illustrativeembodiments are capable of being implemented without requiring a networkconnection to computers located remotely to digital customer marketingenvironment 114.

A merchant, owner, operator, manager or other employee associated withdigital customer marketing environment 114 typically wants to marketproducts or services to customers in the most convenient and efficientmanner possible so as to maximize resulting purchases by the customerand increase sales, profits, and/or revenue. Therefore, the aspects ofthe illustrative embodiments recognize that it is advantageous for themerchant to have as much information as possible describing one or morecustomers and to anticipate items that the customer may wish to purchaseprior to the customer selecting those items for purchase in order toidentify the best items to market to the customer and personalize themerchant's marketing strategy to that particular customer.

Therefore, the illustrative embodiments provide a computer implementedmethod, apparatus, and computer program product for generating acustomer risk assessment score. In one embodiment, the process parsesdynamic data associated with a customer to identify patterns of events.The dynamic data comprises metadata describing an appearance andbehavior of the customer. The process analyzes the patterns of events toidentify risk assessment factors for the customer. The process performsa risk assessment analysis using the risk assessment factors for thecustomer to generate a risk assessment score for the customer, whereinthe risk assessment score indicates a potential risk posed by thecustomer to the retail facility.

In another embodiment, the process receives biometric data for thecustomer from a set of biometric sensors associated with the retailfacility. Biometric data is data describing a set of physiologicalresponses of the customers, a status state of a customer, fingerprints,thumbprints, or a measurement of vital statistics of the customer over agiven period of time.

The vital statistics of the customers include a heart rate of thecustomer, respiratory rate, and/or blood pressure. The set ofphysiological responses includes, without limitation, heart rate, pupildilation, respiration, blood pressure, body temperature, rate ofperspiration, and/or voice stress for the voice of the customer.

The physiological responses are used to determine if one or morecustomers are reacting to external stimuli, such as a marketing message,a display device, an item offered for sale, or any other environmentalelement associated with the retail facility. A change in a biometricreading associated with a customer is compared with a threshold orbaseline reading.

In this embodiment, the process analyzes the biometric data with thedynamic data to identify the risk assessment factors for the customer.If the change in biometric readings exceeds or falls below thethreshold, the process determines that the customer was reacting tosomething in the environment. In response to a determination that thecustomer was viewing an item, an item display, a display device, amarketing message, an employee of the retail facility, another customer,a child, an animal, or some other object when the change in thebiometric reading occurred, the process associates the change in thebiometric reading with the item or the marketing message to form thebiometric data. The change in the biometric readings may also beassociated with a temperature change or odor in the environment. Forexample, if the customer enters a freezer section of the retailfacility, a change in the customer's biometric readings may beattributed to the change in the ambient temperature.

FIG. 2 is a block diagram of a digital customer marketing environment inwhich illustrative embodiments may be implemented. Digital customermarketing environment 200 is a marketing environment, such as digitalcustomer marketing environment 114 in FIG. 1.

Retail facility 202 is a facility for wholly or partially storing,enclosing, or displaying items for marketing, viewing, selection, order,and/or purchase by a customer. For example, retail facility 202 may be,without limitation, a retail store, supermarket, grocery store, amarketplace, a food pavilion, a book store, clothing store, departmentstore, or shopping mall. Retail facility 202 may also include, withoutlimitation, a sports arena, amusement park, water park, conventioncenter, trade center, or any other facility for housing, storing,displaying, offering, providing, and/or selling items. In this example,retail facility 202 is a grocery store or a department store.

Detectors 204-210 are devices for gathering data associated with a setof customers, including, but not limited to, at least one camera, motionsensor device/motion detector, sonar detection device, microphone,sound/audio recording device, audio detection device, a voicerecognition system, a heat sensor/thermal sensor, a seismograph, apressure sensor, a device for detecting odors, scents, and/orfragrances, a radio frequency identification (RFID) tag reader, a globalpositioning system (GPS) receiver, and/or any other detection device fordetecting a presence of a human, animal, object, and/or vehicle locatedoutside of retail facility 202. A set of customers is a set of one ormore customers. A vehicle is any type of vehicle for conveying people,animals, or objects to a destination. A vehicle may include, but is notlimited to, a car, bus, truck, motorcycle, boat, airplane, or any othertype of vehicle.

A heat sensor is any known or available device for detecting heat, suchas, but not limited to, a thermal imaging device for generating imagesshowing thermal heat patterns. A heat sensor can detect body heatgenerated by a human or animal and/or heat generated by a vehicle, suchas an automobile or a motorcycle. A set of heat sensors may include oneor more heat sensors.

A motion detector may be implemented in any type of known or availablemotion detector device. A motion detector device may include, but is notlimited to, one or more motion detector devices using a photo-sensor,radar or microwave radio detector, or ultrasonic sound waves.

A motion detector using ultrasonic sound waves transmits or emitsultrasonic sound waves. The motion detector detects or measures theultrasonic sound waves that are reflected back to the motion detector.If a human, animal, or other object moves within the range of theultrasonic sound waves generated by the motion detector, the motiondetector detects a change in the echo of sound waves reflected back.This change in the echo indicates the presence of a human, animal, orother object moving within the range of the motion detector.

In one example, a motion detector device using a radar or microwaveradio detector may detect motion by sending out a burst of microwaveradio energy and detecting the same microwave radio waves when the radiowaves are deflected back to the motion detector. If a human, animal, orother object moves into the range of the microwave radio energy fieldgenerated by the motion detector, the amount of energy reflected back tothe motion detector is changed. The motion detector identifies thischange in reflected energy as an indication of the presence of a human,animal, or other object moving within the motion detectors range.

A motion detector device, using a photo-sensor, detects motion bysending a beam of light across a space into a photo-sensor. Thephoto-sensor detects when a human, animal, or object breaks orinterrupts the beam of light as the human, animal, or object by movingin-between the source of the beam of light and the photo-sensor. Theseexamples of motion detectors are presented for illustrative purposesonly. A motion detector in accordance with the illustrative embodimentsmay include any type of known or available motion detector and is notlimited to the motion detectors described herein.

A pressure sensor detector may be, for example, a device for detecting achange in weight or mass associated with the pressure sensor. Forexample, if one or more pressure sensors are imbedded in a sidewalk,Astroturf, or floor mat, the pressure sensor detects a change in weightor mass when a human customer or animal steps on the pressure sensor.The pressure sensor may also detect when a human customer or animalsteps off of the pressure sensor. In another example, one or morepressure sensors are embedded in a parking lot, and the pressure sensorsdetect a weight and/or mass associated with a vehicle when the vehicleis in contact with the pressure sensor. A vehicle may be in contact withone or more pressure sensors when the vehicle is driving over one ormore pressure sensors and/or when a vehicle is parked on top of one ormore pressure sensors.

In this example, detectors 204-210 are located at locations along anouter perimeter of digital customer marketing environment 200. However,detectors 204-210 may be located at any position outside retail facility202 to detect customers before the customers enter retail facility 202and/or when customers exit retail facility 202. 063 Detectors 204-210are connected to an analysis server on a data processing system, such asnetwork data processing system 100 in FIG. 1. The analysis server isillustrated and described in greater detail in FIG. 6 below. Theanalysis server includes software for analyzing digital images and otherdata captured by detectors 204-210 to track and/or visually identifyretail items, containers, and/or customers outside retail facility 202.Attachment of identifying marks may be part of this visualidentification in the illustrative embodiments.

In this example, four detectors, detectors 204-210, are located outsideretail facility 202. However, any number of detectors may be used todetect, track, and/or gather dynamic data associated with customersoutside retail facility 202. For example, a single detector, as well astwo or more detectors may be used outside retail facility 202 fortracking customers entering and/or exiting retail facility 202. Thedynamic customer data gathered by the one or more detectors in detectors204-210 is referred to herein as external data.

Camera 212 is an image capture device that may be implemented as anytype of known or available camera, including, but not limited to, avideo camera for taking moving video images, a digital camera capable oftaking still pictures and/or a continuous video stream, a stereo camera,a web camera, and/or any other imaging device capable of capturing aview of whatever appears within the camera's range for remotemonitoring, viewing, or recording of a distant or obscured person,object, or area.

Various lenses, filters, and other optical devices such as zoom lenses,wide angle lenses, mirrors, prisms and the like may also be used withcamera 212 to assist in capturing the desired view. Camera 212 may befixed in a particular orientation and configuration, or it may, alongwith any optical devices, be programmable in orientation, lightsensitivity level, focus or other parameters. Programming data may beprovided via a computing device, such as server 104 in FIG. 1.

Camera 212 may also be a stationary camera and/or non-stationary camera.A non-stationary camera is a camera that is capable of moving and/orrotating along one or more directions, such as up, down, left, right,and/or rotate about an axis of rotation. Camera 212 may also be capableof moving to follow or track a person, animal, or object in motion. Inother words, the camera may be capable of moving about an axis ofrotation in order to keep a customer, animal, or object within a viewingrange of the camera lens. In this example, detectors 204-210 arenon-stationary digital video cameras. Camera 212 may be coupled toand/or in communication with the analysis server. In addition, more thanone image capture device may be operated simultaneously withoutdeparting from the illustrative embodiments of the present invention.

Retail facility 202 may also optionally include set of detectors 213inside retail facility 202. Set of detectors 213 is a set of one or moredetectors, such as detectors 204-210. Set of detectors 213 are detectorsfor gathering dynamic data inside retail facility 202. The dynamic datagathered by set of detectors 213 includes, without limitation, groupingdata, identification data, and/or customer behavior data. The dynamicdata associated with a customer that is captured by one or moredetectors in set of detectors 213 is referred to herein as internaldata.

Set of detectors 213 may be located at any location within retailfacility 202. In addition, set of detectors 213 may include multipledetectors located at differing locations within retail facility 202. Forexample, a detector in set of detectors 213 may be located, withoutlimitation, at an entrance to retail facility 202, on one or moreshelves in retail facility 202, and/or on one or more doors or doorwaysin retail facility 202. In one embodiment, set of detectors 213 includesone or more cameras or other image capture devices for tracking and/oridentifying items, containers for items, shopping containers, customers,shopping companions of the customer, shopping carts, and/or storeemployees inside retail facility 202.

Display devices 214 are multimedia devices for displaying marketingmessages to customers. Display devices 214 may be any type of displaydevice for presenting a text, graphic, audio, video, and/or anycombination of text, graphics, audio, and video to a customer. In thisexample, display devices 214 are located inside retail facility 202.Display devices 214 may be one or more display devices located withinretail facility 202 for use and/or viewing by one or more customers. Theimages shown on display devices 214 are changed in real time in responseto various events such as, without limitation, the time of day, the dayof the week, a particular customer approaching the shelves or rack,items already placed inside container 220 by the customer, and dynamicdata for the customer.

Display devices 216 located outside retail facility 216 include at leastone display device. The display device(s) may be, without limitation, adisplay screen or a kiosk located in a parking lot, queue line, and/orother area outside of retail facility 202. Display devices 216 outsideretail facility 202 may be used in the absence of display devices 214inside retail facility 202 or in addition to display devices 214.

Display device 226 may be operatively connected to a data processingsystem via wireless, infrared, radio, or other connection technologiesknown in the art, for the purpose of transferring data to be displayedon display device 226. The data processing system includes the analysisserver for analyzing dynamic external customer data obtained fromdetectors 204-210 and set of detectors 213, as well as static customerdata obtained from one or more databases storing data associated withcustomers.

Biometric devices 218 are one or more biometric devices for gatheringbiometric data associated with one or more customers. Biometric devices218 include, without limitation, a fingerprint scanner, a retinalscanner, a voice analysis device, a device for measuring heart rate,respiration, blood pressure, body temperature, or a device for capturingany other biometric reading associated with a customer.

Container 220 is a container for holding, carrying, transporting, ormoving one or more items. For example, container 220 may be, withoutlimitation, a shopping cart, a shopping bag, a shopping basket, and/orany other type of container for holding items. In this example,container 220 is a shopping cart. In this example in FIG. 2, only onecontainer 220 is depicted. However, any number of containers may be usedinside and/or outside retail facility 202 for holding, carrying,transporting, or moving items selected by customers.

Container 220 may also optionally include identification tag 224.Identification tag 224 is a tag for identifying container 220, locatingcontainer 220 within digital customer marketing environment 200, eitherinside or outside retail facility 202, and/or associating container 220with a particular customer. For example, identification tag 224 may be aradio frequency identification (RFID) tag, a universal product code(UPC) tag, a global positioning system (GPS) tag, and/or any other typeof identification tag for identifying, locating, and/or tracking acontainer.

Container 220 may also include display device 226 coupled to, mountedon, attached to, or imbedded within container 220. Display device 226 isa multimedia display device for displaying textual, graphical, video,and/or audio marketing messages to a customer. For example, displaydevice 226 may be a digital display screen or personal digital assistantattached to a handle, front, back, or side member of container 220.

Container 220 may optionally include an identification tag reader (notshown) for receiving data from identification tags 230 associated withretail items 228. Retail items 228 are items of merchandise for sale.Retail items 228 may be displayed on a display shelf (not shown) locatedin retail facility 202. Other items of merchandise may be for sale, suchas, without limitation, food, beverages, shoes, clothing, householdgoods, decorative items, or sporting goods, may be hung from displayracks, displayed in cabinets, on shelves, or in refrigeration units (notshown). Any other type of merchandise display arrangement known in theretail trade may also be used in accordance with the illustrativeembodiments. For example, display shelves or racks may include, inaddition to retail items 228, various advertising displays, images, orpostings.

Retail items 228 may be viewed or identified by the illustrativeembodiments using an image capture device or other detector in set ofdetectors 213. To facilitate identification, items may have attachedidentification tags 230. Identification tags 230 are tags associatedwith one or more retail items for identifying the item and/or locationof the item. For example, identification tags 230 may be, withoutlimitation, a bar code pattern, such as a universal product code (UPC)or European article number (EAN), a radio frequency identification(RFID) tag, or other optical identification tag, depending on thecapabilities of the image capture device and associated data processingsystem to process the information and make an identification of retailitems 228. In some embodiments, an optical identification may beattached to more than one side of a given item.

Biometric device 222 is a device coupled or mounted to container 220 forgathering biometric readings associated with the customer usingcontainer 220.

The data processing system, discussed in greater detail in FIG. 3 below,includes associated memory which may be an integral part, such as theoperating memory, of the data processing system or externally accessiblememory. Software for tracking objects may reside in the memory and runon the processor. The software is capable of tracking retail items 228,as a customer removes an item in retail items 228 from its displayposition and places the item into container 220. Likewise, the trackingsoftware can track items which are being removed from container 220 andplaced elsewhere in the retail store, whether placed back in theiroriginal display position or anywhere else including into anothercontainer. The tracking software can also track the position ofcontainer 220 and the customer.

The software can track retail items 228 by using data from one or moreof detectors 204-210 located externally to retail facility, internaldata captured by one or more detectors in set of detectors 213 locatedinternally to retail facility 202, such as identification data receivedfrom identification tags 230 and/or identification data received fromidentification tag 224.

The software in the data processing system keeps a list of which itemshave been placed in each shopping container, such as container 220. Thelist is stored in a database, such as, without limitation, aspreadsheet, relational database, hierarchical database or the like. Thedatabase may be stored in the operating memory of the data processingsystem, externally on a secondary data storage device, locally on arecordable medium such as a hard drive, floppy drive, CD ROM, DVDdevice, remotely on a storage area network, such as storage area network108 in FIG. 1, or in any other type of storage device.

The lists of items in container 220 are updated frequently enough tomaintain a dynamic, accurate, real time listing of the contents of eachcontainer as customers add and remove items from containers, such ascontainer 220. The listings of items in containers are also madeavailable to whatever inventory system is used in retail facility 202.Such listings represent an up-to-the-minute view of which items arestill available for sale, for example, to on-line shopping customers orcustomers physically located at retail facility 202. The listings mayalso provide a demand side trigger back to the supplier of each item. Inother words, the listing of items in customer shopping containers can beused to update inventories, determine current stock available for saleto customers, and/or identification of items that need to be restockedor replenished.

At any time, the customer using container 220 may request to see alisting of the contents of container 220 by entering a query at a userinterface to the data processing system. The user interface may beavailable at a kiosk, computer, personal digital assistant, or othercomputing device connected to the data processing system via a networkconnection. The user interface may also be coupled to a display device,such as, at a display device in display devices 214, display devices216, or display device 226 associated with container 220. The customermay also make such a query after leaving the retail store. For example,a query may be made using a portable device or a home computerworkstation.

The listing is then displayed at a location where it may be viewed bythe customer on a display device. The listing may include the quantityof each item in container 220, as well as the brand, price of each item,discount or amount saved off the regular price of each item, and a totalprice for all items in container 220. Other data may also be displayedas part of the listing, such as, additional incentives to purchase oneor more other items.

When the customer is finished shopping, the customer may proceed to apoint-of-sale checkout station. The checkout station may be coupled tothe data processing system, in which case, the items in container 220are already known to the data processing system due to the dynamiclisting of items in container 220 that is maintained as the customershops in digital customer marketing environment 200. Thus, there is noneed for an employee, customer, or other person to scan each item incontainer 220 to complete the purchase of each item, as is commonly donetoday. In this example, the customer merely arranges for payment of thetotal, for example by use of a smart card, credit card, debit card,cash, or other payment method. In some embodiments, it may not benecessary to empty container 220 at the retail facility at all ifcontainer 220 is a minimal cost item which can be kept by the customer.

In other embodiments, container 220 belongs to the customer. Thecustomer brings container 220 to retail facility 202 at the start of theshopping session. In another embodiment, container 220 belongs to retailfacility 202 and must be returned before the customer leaves digitalcustomer marketing environment 200.

In another example, when the customer is finished shopping, the customermay complete checkout either in-aisle or from a final or terminal-basedcheckout position in the store using a transactional device which may beintegral with container 220 or associated temporarily to container 220.The customer may also complete the transaction using a consumer ownedcomputing device, such as a laptop, cellular telephone, or personaldigital assistant that is connected to the data processing system via anetwork connection.

The customer may also make payment by swiping a magnetic strip on acard, using any known or available radio frequency identification (RFID)enabled payment device, or using a biometric device for identifying thecustomer by the customer's fingerprint, voiceprint, thumbprint, and/orretinal pattern. In such as case, the customer's account isautomatically charged after the customer is identified.

The transactional device may also be a portable device such as a laptopcomputer, palm device, or any other portable device specially configuredfor such in-aisle checkout service, whether integral with container 220or separately operable. In this example, the transactional deviceconnects to the data processing system via a network connection tocomplete the purchase transaction at check out time.

Checkout may be performed in-aisle or at the end of the shopping tripwhether from any point or from a specified point of transaction. Asnoted above, checkout transactional devices may be stationary shareddevices or portable or mobile devices offered to the customer from thestore or may be devices brought to the store by the customer, which arecompatible with the data processing system and software residing on thedata processing system.

Set of speakers 232 is a set of one or more speakers in a sound system.Set of speakers are used to create an ambiance in retail facility 202 byperforming acts such as, without limitation, playing subliminal messagesover a sound system, wherein the subliminal messages encourage theundesirable customer to leave the retail facility, playing music over asound system to encourage the undesirable customer to leave, playingmusic designed to soothe or relax a customer, or other actions.

Set of lights 234 is a set of one or more lights in retail facility 202.Set of lights 234 are used to create an ambiance by performing actionssuch as, but not limited to, shining bright lights in an area of theretail facility occupied by the undesirable customer, shining redlights, flashing lights, softening a lighting level to create a morerelaxed or soothing atmosphere, or other actions.

Thus, in this depicted example, when a customer enters digital customermarketing environment but before the customer enters retail facility202, such as a retail store, the customer is detected and identified byone or more detectors in detectors 204-210 to generate external data.The customer identification may be an exact identification of thecustomer by name, identification by an identifier, or an anonymousidentification that is used to track the customer even though thecustomer's exact name and identity is not known. If the customer takes ashopping container before entering retail facility 202, the shoppingcontainer is also identified. In some embodiments, the customer may beidentified through identification of container 220.

An analysis server in a data processing system associated with retailfacility 202 begins performing data mining on available static customerdata, such as, but not limited to, customer profile information anddemographic information, for use in generating customized marketingmessages targeted to the customer. In one embodiment, the customer ispresented with customized digital marketing messages on one or moredisplay devices in display devices 216 located externally to retailfacility 202 before the customer enters retail facility 202.

The customer is tracked using image data and/or other detection datacaptured by detectors 204-210 as the customer enters retail facility202. The customer is identified and tracked inside retail facility 202by one or more detectors inside the facility, such as set of detectors213.

When the customer enters retail facility 202, the customer is typicallyoffered, provided, or permitted to take shopping container 220 for useduring shopping.

When the customer takes a shopping container, such as container 220, theanalysis server uses data from set of detectors 213, such as,identification data from identification tags 230 and 224, to trackcontainer 220 and items selected by the customer and placed in container220.

As a result, an item selected by the customer, for example, as thecustomer removes the item from its stationary position on a storedisplay, is identified. The selected item may be traced visually by acamera, tracked by another type of detector in set of detectors 213and/or using identification data from identification tags 230. The itemis tracked until the customer places it in container 220 to form aselected item.

Thus, a selected item is identified when a customer removes an item froma store display, such as a shelf, display counter, basket, or hanger. Inanother embodiment, the selected item is identified when the customerplaces the item in the customer's shopping basket, shopping bag, orshopping cart.

Container 220 may contain a digital media display, such as displaydevice 226, mounted on container 220 and/or customer may be offered ahandheld digital media display device, such as a display device indisplay devices 214. In the alternative, the customer may be encouragedto use strategically placed kiosks running digital media marketingmessages throughout retail facility 202. Display device 226, 214, and/or216 may include a verification device for verifying an identity of thecustomer.

For example, display device 214 may include a radio frequencyidentification tag reader 232 for reading a radio frequencyidentification tag, a smart card reader for reading a smart card, or acard reader for reading a specialized store loyalty or frequent customercard. Once the customer has been verified, the data processing systemretrieves past purchase history, total potential wallet-share, shoppersegmentation information, customer profile data, granular demographicdata for the customer, and/or any other available customer data elementsusing known or available data retrieval and/or data mining techniques.These customer data elements are analyzed using at least one data modelto determine appropriate digital media content to be pushed, on-demand,throughout the store to customers viewing display devices 214, 216,and/or display device 226.

The customer is provided with incentives to use display devices 214,216, and/or display device 226 to obtain marketing incentives,promotional offers, and discounts for items. When the customer hasfinished shopping, the customer may be provided with a list of savingsor “tiered” accounting of savings over the regular price of purchaseditems if a display device had not been used to view and use customizeddigital marketing messages.

In this example, a single container and a single customer is described.However, the aspects of the illustrative embodiments may also be used totrack multiple containers and multiple customers simultaneously. In thiscase, the analysis server will store a separate listing of selecteditems for each active customer. As noted above, the listings may bestored in a database. The listing of items in a given container isdisplayed to a customer, employee, agent, or other customer in responseto a query. The listing may be displayed to a customer at any time,either while actively shopping, during check-out, or after the customerleaves retail facility 202.

This process provides an intelligent guided selling methodology tooptimize customer throughput in the store, thereby maximizing oroptimizing total retail content and/or retail sales, profit, and/orrevenue for retail facility 202. It will be appreciated by one skilledin the art that the words “optimize”, “optimization” and related termsare terms of art that refer to improvements in speed and/or efficiencyof a computer program, and do not purport to indicate that a computerprogram has achieved, or is capable of achieving, an “optimal” orperfectly speedy/perfectly efficient state.

Next, FIG. 3 is a block diagram of a data processing system in whichillustrative embodiments may be implemented. Data processing system 300is an example of a computer, such as server 104 or client 110 in FIG. 1,in which computer usable code or instructions implementing the processesmay be located for the illustrative embodiments. In this example, datais transmitted from data processing system 300 to the retail facilityover a network, such as network 102 in FIG. 1. In another embodiment,data processing system 300 is located on-site at the retail facility.

In the depicted example, data processing system 300 employs a hubarchitecture including a north bridge and memory controller hub (MCH)302 and a south bridge and input/output (I/O) controller hub (ICH) 304.Processing unit 306, main memory 308, and graphics processor 310 arecoupled to north bridge and memory controller hub 302. Processing unit306 may contain one or more processors and even may be implemented usingone or more heterogeneous processor systems. Graphics processor 310 maybe coupled to the MCH through an accelerated graphics port (AGP), forexample.

In the depicted example, local area network (LAN) adapter 312 is coupledto south bridge and I/O controller hub 304 and audio adapter 316,keyboard and mouse adapter 320, modem 322, read only memory (ROM) 324,universal serial bus (USB) ports and other communications ports 332, andPCI/PCIe devices 334 are coupled to south bridge and I/O controller hub304 through bus 338, and hard disk drive (HDD) 326 and CD-ROM drive 330are coupled to south bridge and I/O controller hub 304 through bus 340.PCI/PCIe devices may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 324 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 326 and CD-ROM drive330 may use, for example, an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. A super I/O(SIO) device 336 may be coupled to south bridge and I/O controller hub304.

An operating system runs on processing unit 306 and coordinates andprovides control of various components within data processing system 300in FIG. 3. The operating system may be a commercially availableoperating system such as Microsoft® Windows® XP (Microsoft and Windowsare trademarks of Microsoft Corporation in the United States, othercountries, or both). An object oriented programming system, such as theJava™ programming system, may run in conjunction with the operatingsystem and provides calls to the operating system from Java programs orapplications executing on data processing system 300. Java and allJava-based trademarks are trademarks of Sun Microsystems, Inc. in theUnited States, other countries, or both.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as hard disk drive 326, and may be loaded into main memory 308 forexecution by processing unit 306. The processes of the illustrativeembodiments may be performed by processing unit 306 using computerimplemented instructions, which may be located in a memory such as, forexample, main memory 308, read only memory 324, or in one or moreperipheral devices.

In some illustrative examples, data processing system 300 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or customer-generated data. A bus system may be comprised ofone or more buses, such as a system bus, an I/O bus and a PCI bus. Ofcourse the bus system may be implemented using any type ofcommunications fabric or architecture that provides for a transfer ofdata between different components or devices attached to the fabric orarchitecture. A communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter. Amemory may be, for example, main memory 308 or a cache such as found innorth bridge and memory controller hub 302. A processing unit mayinclude one or more processors or CPUs.

Referring now to FIG. 4, a block diagram of a data processing system foranalyzing dynamic data to generate customized marketing messages isshown in accordance with an illustrative embodiment. Data processingsystem 400 is a data processing system, such as data processing system100 in FIG. 1 and/or data processing system 300 in FIG. 3.

Analysis server 402 is any type of known or available server foranalyzing dynamic customer data elements for use in generatingcustomized digital marketing messages. Analysis server 402 may be aserver, such as server 104 in FIG. 1 or data processing system 300 inFIG. 3. Analysis server 402 includes set of data models 404 foranalyzing dynamic customer data elements and static customer dataelements.

Set of data models 404 is one or more data models created a priori orpre-generated for use in analyzing customer data objects forpersonalizing content of marketing messages presented to the customer.Set of data models 404 includes one or more data models for identifyingcustomer data objects and determining relationships between the customerdata objects. The data models in set of data models 404 are generatedusing at least one of a statistical method, a data mining method, acausal model, a mathematical model, a marketing model, a behavioralmodel, a psychological model, a sociological model, or a simulationmodel.

Profile data 406 is data regarding one or more customers. In thisexample, profile data 406 includes point of contact data, profiled pastdata, current actions data, transactional history data, certainclick-stream data, granular demographics 408, psychographic data 410,registration e.g. customer provided data, and account data and/or anyother data regarding a customer.

Point of contact data is data regarding a method or device used by acustomer to interact with a data processing system of a merchant orsupplier and/or receive customized marketing message 430 for display.The customer may interact with the merchant or supplier using acomputing device or display terminal having a user interface forinputting data and/or receiving output. The device or terminal may be adevice provided by the retail facility and/or a device belonging to orprovided by the customer. For example, the display or access device mayinclude, but is not limited to, a cellular telephone, a laptop computer,a desktop computer, a computer terminal kiosk, or a personal digitalassistant (PDA).

If display device 432 is a display device associated with the retailfacility, details and information regarding display device 432 will beknown to analysis server 402. However, if display device 432 is adisplay device belonging to the customer or brought to the retailfacility by the customer, analysis server 402 may identify the type ofdisplay device using techniques such as interrogation commands, cookies,or any other known or equivalent technique. From the type of deviceother constraints may be determined such as display size, resolution,refresh rate, color capability, keyboard entry capability, other entrycapability such as pointer or mouse, speech recognition and response,language constraints, and any other fingertip touch point constraintsand assumptions about customer state of the display device. For example,someone using a cellular phone may have a limited time window for makingphone calls and be sensitive to location and local time of day, whereasa casual home browser may have a greater luxury of time and fasterconnectivity.

An indication of a location for the point of contact may also bedetermined. For example, global positioning system (GPS) coordinates ofthe customer may be determined if the customer device has such acapability whether by including a real time global positioning systemreceiver or by periodically storing global positioning systemcoordinates entered by some other method. Other location indications mayalso be determined such as post office address, street or crossroadcoordinates, latitude-longitude coordinates or any other locationindicating system.

Analysis server 402 may also determine the connectivity associated withthe customer's point of contact. For example, the customer may beconnected to the merchant or supplier in any of a number ways such as amodem, digital modem, network, wireless network, Ethernet, intranet, orhigh speed lines including fiber optic lines. Each way of connectionimposes constraints of speed, latency, and/or mobility which can thenalso be determined.

The profiled past comprises data that may be used, in whole or in part,for individualization of customized marketing message 430. Globalprofile data may be retrieved from a file, database, data warehouse, orany other data storage device. Multiple storage devices and software mayalso be used to store profile data 406. Some or all of the data may beretrieved from the point of contact device, as well. The profiled pastmay comprise an imposed profile, global profile, individual profile, anddemographic profile. The profiles may be combined or layered to definethe customer for specific promotions and marketing offers.

In the illustrative embodiments, a global profile includes data on thecustomer's interests, preferences, and affiliations. The profiled pastmay also comprise retrieving purchased data. Various firms provide datafor purchase which is grouped or keyed to presenting a lifestyle or lifestage view of customers by block or group or some other baselineparameter. The purchased data presents a view of one or more customersbased on aggregation of data points such as, but not limited togeographic block, age of head of household, income level, number ofchildren, education level, ethnicity, and purchasing patterns.

The profiled past may also include navigational data relating to thepath the customer used to arrive at a web page which indicates where thecustomer came from or the path the customer followed to link to themerchant or supplier's web page. Transactional data of actions taken isdata regarding a transaction. For example, transaction data may includedata regarding whether the transaction is a first time transaction or arepeat transaction, and/or how much the customer usually spends.Information on how much a customer generally spends during a giventransaction may be referred to as basket share. Data voluntarilysubmitted by the customer in responding to questions or a survey mayalso be included in the profiled past.

Current actions, also called a current and historical record, are alsoincluded in profile data 406. Current actions are data defining customerbehavior. One source of current actions is listings of the purchasesmade by the customer, payments and returns made by the customer, and/orclick-stream data from a point of contact device of the customer.Click-stream data is data regarding a customer's navigation of an onlineweb page of the merchant or supplier. Click-stream data may include pagehits, sequence of hits, duration of page views, response toadvertisements, transactions made, and conversion rates. Conversion rateis the number of times the customer takes action divided by the numberof times an opportunity is presented.

In this example, profiled past data for a given customer is stored inanalysis server 402. However, in accordance with the illustrativeembodiments, profiled past data may also be stored in any local orremote data storage device, including, but not limited to, a device suchas storage area network 108 in FIG. 1 or read only memory (ROM) 324and/or compact disk read only memory (CD-ROM) 330 in FIG. 3.

Granular demographics 408 is a source of static customer data elements.Static customer data elements are data elements that do not tend tochange in real time, such as a customer's name, date of birth, andaddress. Granular demographics 408 provides a detailed demographicsprofile for one or more customers. Granular demographics 408 mayinclude, without limitation, ethnicity, block group, lifestyle, lifestage, income, and education data. Granular demographics 408 may be usedas an additional layer of profile data 406 associated with a customer.

Psychographic data 410 refers to an attitude profile of the customer.Examples of attitude profiles include, without limitation, a trendbuyer, a time-strapped person who prefers to purchase a complete outfit,a cost-conscious shopper, a customer that prefers to buy in bulk, or aprofessional buyer who prefers to mix and match individual items fromvarious suppliers.

Dynamic data 412 is data that includes dynamic customer data elementsthat are changing in real-time. For example, dynamic customer dataelements could include, without limitation, the current contents of acustomer's shopping basket, the time of day, the day of the week,whether it is the customer's birthday or other holiday observed by thecustomer, customer's responses to marketing messages and/or items viewedby the customer, customer location, the customer's current shoppingcompanions, the speed or pace at which the customer is walking throughthe retail facility, and/or any other dynamically changing customerinformation. Dynamic data 412 includes external data, grouping data,customer identification data, customer behavior data, and/or currentevents data.

Dynamic data 412 is processed and/or analyzed to generate customizedmarketing messages. Processing dynamic data 412 includes, but is notlimited to, filtering dynamic data 412 for relevant data elements,combining dynamic data 412 with other dynamic customer data elements,comparing dynamic data 412 to baseline or comparison models for externaldata, and/or formatting dynamic data 412 for utilization and/or analysisin one or more data models in set of data models 404. The processeddynamic data 412 is analyzed and/or further processed using one or moredata models in set of data models 404.

Dynamic data 412 may include customer identification data. Customeridentification data identifies the customer without human input. In thiscase, the customer identification data may be generated by performing,without limitation, facial recognition analysis on an image of a face ofthe customer, license plate recognition analysis on an image of avehicle license plate, a fingerprint analysis on a fingerprint of thecustomer, and voice analysis on a sound file. A customer profile canthen be retrieved from profile data 406 using the customeridentification data in dynamic data 412.

Biometric data 414 is captured by a set of one or more biometric devicesassociated with a customer. Biometric devices include, withoutlimitation, a fingerprint scanner, a retinal scanner, a voice analysisdevice, a device for measuring heart rate, respiration, blood pressure,body temperature, or a device for capturing any other biometric readingassociated with a customer. The biometric data is gathered in real-timeas the customer is shopping at the retail facility. Biometric data 414is received by analysis server 402 from the set of biometric devices.The biometric data is data describing a set of physiological responsesof the customer.

Biometric readings associated with the customer that are captured by thebiometric device(s) are analyzed by analysis server 402 to identifybiometric readings that exceed a threshold change to form biometric data414. If the customer was viewing an item or a marketing message when thechange in the biometric reading occurred, analysis server 402 associatesthe change in the biometric reading with the item or the marketingmessage to form biometric data 414. If the customer was interacting withanother customer, an employee of the retail facility, a child, or ananimal, analysis server 402 associates the change in the biometricreading with the another customer, the employee, the child, or theanimal to form biometric data 414.

Threshold 420 is a threshold risk assessment score that is used todetermine when risk assessment score 422 indicates a customer poses apotential threat to the store. The potential threat posed by thecustomer to the retail facility includes, but is not limited to, a riskof the customer shoplifting, stealing from other customers or employees,committing theft from the store or other customers, committing violenceon employees, other customers, or self-inflicted violence, failing topay bills, defaulting on loans, disrupting operations of the retailfacility, criminal activities, threatening customers, panhandling, andloitering.

Risk assessment engine 421 is software for performing a risk assessmentanalysis of a customer. In this example, analysis server 402 parsesdynamic data 412 associated with a customer to identify patterns ofevents. Dynamic data 412 includes metadata describing an appearance andbehavior of the customer. Risk assessment engine 421 analyzes thepatterns of events to identify risk assessment factors for the customer.Risk assessment engine 421 performs a risk assessment analysis using therisk assessment factors for the customer to generate risk assessmentscore 422 for the customer. Risk assessment score 422 is a ranking thatindicates a potential risk posed by the customer to the retail facility.

Risk assessment engine 421 also retrieves a customer profile for thecustomer. The customer profile includes static customer data elementsdescribing the customer, such as, but not limited to, the customer'scriminal record, credit rating, past incidents in the retail store, andother details regarding the customer's past actions and record. Riskassessment engine 421 analyzes dynamic data 412 and biometric data 414,with the customer profile data to identify the risk assessment factorsfor the customer.

In another embodiment, risk assessment engine 421 analyzes the riskassessment factors using at least one of a statistical method, a datamining method, and pre-generated manual input to generate weighted riskfactors. Risk assessment engine 421 generates risk assessment score 422using the weighted risk assessment factors and cohort data for thecustomer. Cohort data is data describing the customer, such as thecustomer's appearance and behavior. The cohort data may describe thecustomer as wearing a trench coat in warm weather or wearing sunglassesindoors.

If dynamic data 412 includes grouping data that indicates the customeris shopping with one or more other people or animals, risk assessmentscore 422 is generated for each member of the group. Grouping data forthe customer describes a group associated with the customer. The groupmay be, for example, a group of parents with children, teenagers,children, minors unaccompanied by adults, minors accompanied by adults,grandparents with grandchildren, senior citizens, couples, friends,coworkers, a customer shopping with a pet, a customer with a large dog,a customer with an unrestrained animal, and a customer shopping alone.

If risk assessment score 422 for the customer is greater than threshold420, risk assessment engine 421 identifies the customer as anundesirable customer that may pose a potential threat to the store. Inresponse, risk assessment engine 421 initiates aggressive marketingdisincentives targeted towards to the undesirable customer. Aggressivemarketing disincentives are marketing initiatives intended to decreasean amount of time the customer spends shopping in the retail facility.

The aggressive disincentives include, without limitation, informing aset of employees associated with the retail facility that the customeris an undesirable customer and directing the set of employees to avoidoffering assistance unless assistance is requested by the customer,providing disincentive marketing messages to the customer that includeuncompetitive product pricing and undesirable product offers, andcreating a negative ambiance in an area of the retail facilityassociated with the customer. Creating a negative ambiance furthercomprises shining harsh or bright lights in an area of the retailfacility occupied by the customer, playing subliminal messages over asound system that encourage or prompt the customer to leave the retailfacility, playing music over a sound system, wherein the music isdesigned to encourage the customer to feel uncomfortable, and/oradjusting a temperature in an area of the retail facility to anuncomfortable temperature, wherein an uncomfortable temperature is atleast one of a temperature that is colder than a predeterminedtemperature, higher than a predetermined comfortable temperature, and ahumidity that is higher than a predetermined comfortable humidity level.

If risk assessment score 422 indicates the customer is a highlydesirable customer, risk assessment engine initiates marketingincentives targeted towards the customer. The marketing incentivesinclude, without limitation, notifying an employee associated with theretail facility to assist the customer and generating customizedmarketing messages for the customer that include competitive productpricing and preferred product offers. A display device may also beprovided to the customer that provides a map and/or locations of itemsin the retail facility to improve a shopping experience of the customer.

If risk assessment score 422 indicates the customer is a neutral ormoderately desirable customer, risk assessment engine 421 initiatesmoderate marketing efforts directed towards the customer that arecheaper to generate and present to the customer than aggressivemarketing incentives.

Content server 423 is any type of known or available server for storingmodular marketing messages 424. Content server 423 may be a server, suchas server 104 in FIG. 1 or data processing system 300 in FIG. 3.

Modular marketing messages 424 are two or more self contained marketingmessages that may be combined with one or more other modular marketingmessages in modular marketing messages 424 to form a customizedmarketing message for display to the customer. Modular marketingmessages 424 can be quickly and dynamically assembled and disseminatedto the customer in real-time.

In this illustrative example, modular marketing messages 424 arepre-generated. In other words, modular marketing messages 424 arepreexisting marketing message units that are created prior to analyzingdynamic data 412 associated with a customer using one or more datamodels to generate a personalized marketing message for the customer.Two or more modular marketing messages are combined to dynamicallygenerate customized marketing message 430, customized or personalizedfor a particular customer. Although modular marketing messages 424 arepre-generated, modular marketing messages 424 may also include templatesimbedded within modular marketing messages for adding personalizedinformation, such as a customer's name or address, to the customizedmarketing message.

Derived marketing messages 426 is a software component for determiningwhich modular marketing messages in modular marketing messages 424should be combined or utilized to dynamically generate customizedmarketing message 430 for the customer in real time. Derived marketingmessages 426 uses the output generated by analysis server 402 as aresult of analyzing dynamic data 412 associated with a customer usingone or more appropriate data models in set of data models 404 toidentify one or more modular marketing messages for the customer. Theoutput generated by analysis server 402 from analyzing dynamic data 412using appropriate data models in set of data models 404 includesmarketing message criteria for the customer.

In other words, dynamic data 412 is analyzed to generate personalmarketing message criteria. Derived marketing messages 426 uses themarketing message criteria for the customer to select one or moremodular marketing messages in modular marketing messages 424.

A customized marketing message is generated using personalized marketingmessage criteria that are identified using the dynamic data.Personalized marketing message criteria are criterion or indicators forselecting one or more modular marketing messages for inclusion in thecustomized marketing message. The personalized marketing messagecriteria may include one or more criterion. The personalized marketingmessage criteria may be generated, in part, a priori or pre-generatedand in part dynamically in real-time based on the dynamic data for thecustomer and/or any available static customer data associated with thecustomer. Dynamic data 412 includes external data gathered outside theretail facility and/or dynamic data gathered inside the retail facility.

If an analysis of dynamic data 412 indicates that the customer isshopping with a large dog, the personal marketing message criteria mayinclude criteria to indicate marketing of pet food and items for largedogs. Because people with large dogs often have large yards, thepersonal marketing message criteria may also indicate that yard items,such as yard fertilizer, weed killer, or insect repellant may should bemarketed. The personal marketing message criteria may also indicatemarketing elements designed to appeal to animal lovers and pet owners,such as incorporating images of puppies, images of dogs, phrases such as“man's best friend”, “puppy love”, advice on pet care and dog health,and/or other pet friendly images, phrases, and elements to appeal to thecustomer's tastes and interests.

Derived marketing messages 426 uses the output of one or more datamodels in set of data models 404 that were used to analyze dynamic data412 associated with a customer to identify one or more modular marketingmessages to be combined together to form the personalized marketingmessage for the customer.

For example, a first modular marketing message may be a special on amore expensive brand of peanut butter. A second modular marketingmessage may be a discount on jelly when peanut butter is purchased. Inresponse to marketing message criteria that indicates the customerfrequently purchases cheaper brands of peanut butter, the customer haschildren, and the customer is currently in an aisle of the retailfacility that includes jars of peanut butter, derived marketing messages426 will select the first marketing message and the second marketingmessage based on the marketing message criteria for the customer.

Dynamic marketing message assembly 428 is a software component forcombining the one or more modular marketing messages selected by derivedmarketing messages 426 to form customized marketing message 430. Dynamicmarketing message assembly 428 combines modular marketing messagesselected by derived marketing messages 426 to create appropriatecustomized marketing message 430 for the customer. In the example above,after derived marketing messages 426 selects the first modular marketingmessage and the second modular marketing message based on the marketingmessage criteria, dynamic marketing message assembly 428 combines thefirst and second modular marketing messages to generate a customizedmarketing message offering the customer a discount on both the peanutbutter and jelly if the customer purchases the more expensive brand ofpeanut butter. In this manner, dynamic marketing message assembly 428provides assembly of customized marketing message 430 based on outputfrom the data models analyzing dynamic data.

Customized marketing message 430 is a unique one-to-one customizedmarketing message for a specific customer. Customized marketing message430 is generated using dynamic data 412 and/or static customer dataelements, such as the customer's demographics and psychographics, toachieve this unique one-to-one marketing.

Customized marketing message 430 is generated for a particular customerbased on dynamic customer data elements, such as grouping data, customeridentification data, current events data, and customer behavior data.For example, if modular marketing messages 424 include marketingmessages identified by numerals 1-20, customized marketing message 430may be generated using marketing messages 2, 8, 9, and 19. In thisexample, modular marketing messages 2, 8, 9, and 19 are combined tocreate a customized marketing message that is generated for display tothe customer rather than displaying the exact same marketing messages toall customers. Customized marketing message 430 is displayed on displaydevice 432.

Customized marketing message 430 may include advertisements, sales,special offers, incentives, opportunities, promotional offers, rebateinformation and/or rebate offers, discounts, and opportunities. Anopportunity may be a “take action” opportunity, such as asking thecustomer to make an immediate purchase, select a particular item,request a download, provide information, or take any other type ofaction.

Customized marketing message 430 may also include content or messagespushing advertisements and opportunities to effectively andappropriately drive the point of contact customer to some conclusion orreaction desired by the merchant.

Customized marketing message 430 is formed in a dynamic closed loopmanner in which the content delivery depends on dynamic data 412, aswell as other dynamic customer data elements and static customer data,such as profile data 406 and granular demographics 408. Therefore, allinterchanges with the customer may sense and gather data associated withcustomer behavior, which is used to generate customized marketingmessage 430.

Display device 432 is a multimedia display for presenting customizedmarketing messages to one or more customers. Display device 432 may be amultimedia display, such as, but not limited to, display devices 214,216, and 226 in FIG. 2. Display device 432 may be, for example, apersonal digital assistant (PDA), a cellular telephone with a displayscreen, an electronic sign, a laptop computer, a tablet PC, a kiosk, adigital media display, a display screen mounted on a shopping container,and/or any other type of device for displaying digital messages to acustomer.

Thus, a merchant has a capability for interacting with the customer on adirect one-to-one level by sending customized marketing message 430 todisplay device 432. Customized marketing message 430 may be sent anddisplayed to the customer via a network. For example, customizedmarketing message 430 may be sent via a web site accessed as a uniqueuniform resource location (URL) address on the World Wide Web, as wellas any other networked connectivity or conventional interactionincluding, but not limited to, a telephone, computer terminal, cellphone or print media.

Display device 432 may be a display device mounted on a shopping cart, ashopping basket, a shelf or compartment in a retail facility, includedin a handheld device carried by the customer, or mounted on a wall inthe retail facility. In response to displaying customized marketingmessage 430, a customer can select to print the customized marketingmessage 430 as a coupon and/or as a paper or hard copy for later use. Inanother embodiment, display device 432 automatically prints customizedmarketing message 430 for the customer rather than displaying customizedmarketing message 430 on a display screen or in addition to displayingcustomized marketing message 430 on the display screen.

In another embodiment, display device 432 provides an option for acustomer to save customized marketing message 430 in an electronic formfor later use. For example, the customer may save customized marketingmessage 430 on a hand held display device, on a flash memory, a customeraccount in a data base associated with analysis server 402, or any otherdata storage device. In this example, when customized marketing message430 is displayed to the customer, the customer is presented with a “useoffer now” option and a “save offer for later use” option. If thecustomer chooses the “save offer” option, the customer may save anelectronic copy of customized marketing message 430 and/or print a papercopy of customized marketing message 430 for later use. In this example,customized marketing message 430 is generated and delivered to thecustomer.

FIG. 5 is a block diagram of a dynamic marketing message assemblytransmitting a customized marketing message to a set of display devicesin accordance with an illustrative embodiment. Dynamic marketing messageassembly 500 is a software component for combining two or more modularmarketing messages into a customized marketing message for a customer.Dynamic marketing message assembly 500 may be a component such asdynamic marketing message assembly 428 in FIG. 4.

Dynamic marketing message assembly 500 transmits a customized marketingmessage, such as customized marketing message 430 in FIG. 4, to one ormore display devices in a set of display devices. In this example, theset of display devices includes, but is not limited to, digital mediadisplay device 502, kiosk 504, personal digital assistant 506, cellulartelephone 508, and/or electronic sign 510. A set of display devices inaccordance with the illustrative embodiments may include any combinationof display devices and any number of each type of display device. Forexample, a set of display devices may include, without limitation, sixkiosks, fifty personal digital assistants, and no cellular telephones.In another example, the set of display devices may include electronicsigns and kiosks but no personal digital assistants or cellulartelephones.

Digital media display device 502 is any type of known or availabledigital media display device for displaying a marketing message. Digitalmedia display device 502 may include, but is not limited to, a monitor,a plasma screen, a liquid crystal display screen, and/or any other typeof digital media display device.

Kiosk 504 is any type of known or available kiosk. In one embodiment, akiosk is a structure having one or more open sides, such as a booth. Thekiosk includes a computing device associated with a display screenlocated inside or in association with the structure. The computingdevice may include a user interface for a user to provide input to thecomputing device and/or receive output. For example, the user interfacemay include, but is not limited to, a graphical user interface (GUI), amenu-driven interface, a command line interface, a touch screen, a voicerecognition system, an alphanumeric keypad, and/or any other type ofinterface.

Personal digital assistant 506 is any type of known or availablepersonal digital assistant (PDA). Cellular telephone 508 is any type ofknown or available cellular telephone and/or wireless mobile telephone.Cellular telephone 508 includes a display screen that is capable ofdisplaying pictures, graphics, and/or text. Additionally, cellulartelephone 508 may also include an alphanumeric keypad, joystick, and/orbuttons for providing input to cellular telephone 508. The alphanumerickeypad, joystick, and/or buttons may be used to initiate variousfunctions in cellular telephone 508. These functions include forexample, activating a menu, displaying a calendar, receiving a call,initiating a call, displaying a customized marketing message, saving acustomized marketing message, and/or selecting a saved customizedmarketing message.

Electronic sign 510 is any type of electronic messaging system. Forexample, electronic sign 510 may include, without limitation, an outdoorelectronic light emitting diode (LED) display, moving message boards,variable message signs, tickers, electronic message centers, videoboards, and/or any other type of electronic signage.

The display device may also include, without limitation, a laptopcomputer, a smart watch, a digital message board, a monitor, a tabletPC, a printer for printing the customized marketing message on a papermedium, or any other output device for presenting output to a customer.

A display device may be located externally to the retail facility todisplay marketing messages to the customer before the customer entersthe retail facility. In another embodiment, the customized marketingmessage is displayed to the customer on a display device inside theretail facility after the customer enters the retail facility and beginsshopping.

Turning now to FIG. 6, a block diagram of an identification tag readerfor identifying items selected by a customer is shown in accordance withan illustrative embodiment. Item 600 is any type of item, such as retailitems 228 in FIG. 2. Identification tag 602 associated with item 600 isa tag for providing information regarding item 600 to identification tagreader 604. Identification tag 602 is a tag such as a tag inidentification tags 230 in FIG. 2. Identification tag 602 may be a barcode, a radio frequency identification tag, a global positioning systemtag, and/or any other type of tag.

Radio Frequency Identification tags include read-only identificationtags and read-write identification tags. A read-only identification tagis a tag that generates a signal in response to receiving an interrogatesignal from an item identifier. A read-only identification tag does nothave a memory. A read-write identification tag is a tag that responds towrite signals by writing data to a memory within the identification tag.A read-write tag can respond to interrogate signals by sending a streamof data encoded on a radio frequency carrier. The stream of data can belarge enough to carry multiple identification codes. In this example,identification tag 602 is a radio frequency identification tag.

Identification tag reader 604 is any type of known or available devicefor retrieving information from identification tag 602. Identificationtag reader 604 may be, but is not limited to, a radio frequencyidentification tag reader or a bar code reader, such as identificationtag reader 232 in FIG. 2. A bar code reader is a device for reading abar code, such as a universal product code. In this example,identification tag reader 604 provides identification data 606, itemdata 610, and/or location data 612 to an analysis server, such asanalysis server 402 in FIG. 4.

Identification data 608 is data regarding the product name and/ormanufacturer name of item 600 selected for purchase by a customer. Itemdata 610 is information regarding item 600, such as, without limitation,the regular price, sale price, product weight, and/or tare weight foritem 600. Identification data 608 is used to identify items selected bythe customer for purchase.

Location data 612 is data regarding a location of item 600 within theretail facility and/or outside the retail facility. For example, ifidentification tag 602 is a bar code, the item associated withidentification tag 602 must be in close physical proximity toidentification tag reader 604 for a bar code scanner to read a bar codeon item 600. Therefore, location data 612 is data regarding the locationof identification tag reader 604 currently reading identification tag602. However, if identification tag 602 is a global positioning systemtag, a substantially exact or precise location of item 600 may beobtained using global positioning system coordinates obtained from theglobal positioning system tag.

Identifier database 606 is a database for storing any information thatmay be needed by identification tag reader 604 to read identificationtag 602. For example, if identification tag 602 is a radio frequencyidentification tag, identification tag will provide a machine readableidentification code in response to a query from identification tagreader 604. In this case, identifier database 606 stores descriptionpairs that associate the machine readable codes produced byidentification tags with human readable descriptors. For example, adescription pair for the machine readable identification code“10141014111111” associated with identification tag 602 would be pairedwith a human readable item description of item 600, such as “orangejuice.” An item description is a human understandable description of anitem. Human understandable descriptions are for example, text, audio,graphic, or other representations suited for display or audible output.

FIG. 7 is a block diagram illustrating an external marketing manager forgenerating current events data in accordance with an illustrativeembodiment. External marketing manager 700 is a software component forcollecting current news items 702, competitor marketing data 704,holidays, seasonal events, seasonal celebrations, and/or events data706, and/or any other current events or news data from a set of sources.The set of sources may include one or more sources. A source may be,without limitation, a newspaper, catalog, a web page or other networkresource, a television program or commercial, a flier, a pamphlet, abook, a booklet, a news board, a coupon board, a news group, a blog, amagazine, a religious calendar, a secular calendar, or any other paperor electronic source of information. A source may also includeinformation provided by a human user.

External marketing manager 700 stores current news items 702, competitormarketing data 704, holidays and/or events data 706, and/or any othercurrent events or news data in data storage device 708 as externalmarketing data 710. Data storage device 708 may be implemented as anytype of data storage device, including, without limitation, a hard disk,a database, a main memory, a flash memory, a random access memory (RAM),a read only memory (ROM), or any other data storage device.

In this example, external marketing manager 700 filters or processesexternal marketing data 710 to form current events data 720. Filteringexternal marketing data 710 may include selecting data items or dataobjects associated with marketing one or more items to a customer. Adata item or data object associated with marketing one or more items isa data element that may influence a customer's decision to purchase aproduct. For example, the occurrence of a sporting event may influencethe items purchased by a customer, such as pizza, potato chips, beer,and big screen television sets.

A data element indicating the occurrence of a holiday or religiousevent, such as Christmas or Thanksgiving, may also influence the itemspurchased by a customer. For example, as Thanksgiving approaches,customers are more likely to purchase turkey and pumpkin pie. At Easter,customers are more likely to purchase ham, chocolate bunnies, and Eastereggs.

A data element indicating that a storm or hurricane is approaching mayinfluence projects such as installing storm shutters and generators.These data elements that may influence customer purchases and sales ofitems are selected to form current events data 720. Current events data720 is then sent to an analysis server, such as analysis server 402 inFIG. 4 for use in identifying items likely to be of interest tocustomers, as well as personalizing marketing messages to a customer.

In this example, external marketing manager 700 filters externalmarketing data 710 for relevant data elements to form current eventsdata 720 without intervention by a human user. In another embodiment, ahuman user filters external marketing data 710 manually to generatecurrent events data 720. The analysis server uses the current eventsdata to identify an event of interest to the customer that occurs withina predetermined period of time. For example, if a customer profile anddynamic data indicates that the customer is Catholic and current eventsdata 720 indicates Mardi Gras is approaching, the analysis server canidentify items associated with Mardi Gras, such as King Cake, Mardi Grasbeads, and masks.

In this example, an analysis server processes dynamic data with thecurrent events data to identifying events associated with the customerand related to events in current events data 720 indicating a potentialrisk to the retail facility associated with the customer. For example,if current events data 720 indicates that a gunman driving a black vanand wearing a long black coat has been attacking customers in parkinglots, a risk assessment engine can use this current events data tosearch for a person matching the description of the gunman. If dynamicdata indicates a person with a gun and driving a black van is in theparking lot, the risk assessment engine generates a warning to storesecurity and the police. The risk assessment engine may also initiateother security measures such as locking doors of the retail facility,sounding an alarm, or other measures to protect customers.

FIG. 8 is a block diagram illustrating a smart detection engine forgenerating customer identification data and selected item data inaccordance with an illustrative embodiment. Smart detection system 800is a software architecture for analyzing camera images and otherdetection data to form dynamic data 820. In this example, the detectiondata is video images captured by a camera. However, the detection datamay also include, without limitation, pressure sensor data captured by aset of pressure sensors, heat sensor data captured by a set of heatsensors, motion sensor data captured by a set of motion sensors, audiocaptured by an audio detection device, such as a microphone, or anyother type of detection data described herein.

Audio/video capture device 802 is a device for capturing video imagesand/or capturing audio. Audio/video capture device 802 may be, but isnot limited to, a digital video camera, a microphone, a web camera, orany other device for capturing sound and/or video images.

Audio data 804 is data associated with audio captured by audio/videocapture device 802, such as human voices, vehicle engine sounds, dogbarking, horns, and any other sounds. Audio data 804 may be a soundfile, a media file, or any other form of audio data. Audio/video capturedevice 802 captures audio associated with a set of one or more customersinside a retail facility and/or outside a retail facility to form audiodata 804.

Video data 806 is image data captured by audio/video capture device 802.Video data 806 may be a moving video file, a media file, a stillpicture, a set of still pictures, or any other form of image data. Videodata 806 is video or images associated with a set of one or morecustomers inside a retail facility and/or outside a retail facility.

For example, video data 806 may include images of a customer's face, animage of a part or portion of a customer's car, an image of a licenseplate on a customer's car, and/or one or more images showing acustomer's behavior. An image showing a customer's behavior orappearance may show a customer wearing a long coat on a hot day, acustomer walking with two small children which may be the customer'schildren or grandchildren, a customer moving in a hurried or leisurelymanner, or any other type of behavior or appearance attributes of acustomer, the customer's companions, or the customer's vehicle.

Audio/video capture device 802 transmits audio data 804 and video data806 to smart detection engine 808. Audio data 804 and video data 806 maybe referred to as detection data. Smart detection engine 808 is softwarefor analyzing audio data 804 and video data 806. In this example, smartdetection engine 808 processes audio data 804 and video data 806 intodata and metadata to form dynamic data 820. Dynamic data 820 includes,but not limited to, external data 810, customer identification data 814,grouping data 816, customer event data 818, and current events data 822.Customer grouping data is data describing a customer's companions, suchas children, parents, siblings, peers, friends, and/or pets.

Processing the audio data 804 and video data 806 may include filteringaudio data 804 and video data 806 for relevant data elements, analyzingaudio data 804 and video data 806 to form metadata describing orcategorizing the contents of audio data 804 and video data 806, orcombining audio data 804 and video data 806 with other audio data, videodata, and data associated with a group of customers received fromcameras.

Smart detection engine 808 uses computer vision and pattern recognitiontechnologies to analyze audio data 804 and/or video data 806. Smartdetection engine 808 includes license plate recognition technology whichmay be deployed in a parking lot or at the entrance to a retail facilitywhere the license plate recognition technology catalogs a license plateof each of the arriving and departing vehicles in a parking lotassociated with the retail facility.

Smart detection engine 808 includes behavior analysis technology todetect and track moving objects and classify the objects into a numberof predefined categories. As used herein, an object may be a humancustomer, an item, a container, a shopping cart or shopping basket, orany other object inside or outside the retail facility. Behavioranalysis technology could be deployed on various cameras overlooking aparking lot, a perimeter, or inside a facility.

Face detection/recognition technology may be deployed in parking lots,at entry ways, and/or throughout the retail facility to capture andrecognize faces. Badge reader technology may be employed to read badges.Radar analytics technology may be employed to determine the presence ofobjects. Events from access control technologies can also be integratedinto smart detection engine 808.

The events from all the above detection technologies are cross indexedinto a single repository, such as multi-mode database. In such arepository, a simple time range query across the modalities will extractlicense plate information, vehicle appearance information, badgeinformation, and face appearance information, thus permitting an analystto easily correlate these attributes.

Smart detection system 800 may be implemented using any known oravailable software for performing voice analysis, facial recognition,license plate recognition, and sound analysis. In this example, smartdetection system 800 is implemented as IBM® smart surveillance system(S3) software.

The data gathered from the behavior analysis technology, license platerecognition technology, face detection/recognition technology, badgereader technology, radar analytics technology, and any other video/audiodata received from a camera or other video/audio capture device isreceived by smart detection engine 808 for processing into dynamic data820.

FIG. 9 is a block diagram of a shopping container in accordance with anillustrative embodiment. Shopping container 900 is a container forcarrying, moving, or holding items selected by a customer, such ascontainer 220 in FIG. 2. In this example, container 900 is a shoppingcart.

Display device 902 is a multimedia display device for presenting ordisplaying customized digital marketing messages to one or morecustomers, such as display devices 226 in FIG. 2 and/or display device430 in FIG. 4. In this example, display device is coupled to shoppingcontainer 900. Display device 902 displays customized digital marketingmessages received from a derived marketing messages device, such asderived marketing messages 626 in FIG. 6.

Biometric device 904 is any type of known or available device formeasuring a physiological response or trait associated with a customer.Biometric device 904 is a biometric device, such as, without limitation,biometric device 222 in FIG. 2. Biometric device 904 may be a biometricdevice for measuring a customer's heart rate over a given period oftime, a change in voice stress for the customer's voice, a change inblood pressure, and/or a change in pupil dilation that does notcorrelate or correspond to a change in an ambient lighting level.

In this example, biometric device 904 is coupled to shopping container900. Biometric device 904 monitors biometric readings of a customer anddetects changes in the biometric readings of the customer that exceeds athreshold change. In this example, biometric device 904 is a device formeasuring a customer's heart rate over time. Biometric device 904obtains the customer's pulse rate by measuring the customer's fingerpulse.

In another embodiment, biometric device 904 may also identify a customerbased on a fingerprint scan, voiceprint analysis, and/or retinal scan.For example, biometric device 904 may dynamically identify the customerby scanning the customer's fingerprint and/or analyzing fingerprint dataassociated with the customer to determine the customer's identity. Inone example, biometric device 904 may, but is not required to, connectedto a remote data storage device storing data to retrieve customerfingerprint data for use in identifying a given customer using thecustomer's fingerprint. Biometric device 904 may be connected to theremote data storage device via a wireless network connection, such asnetwork 102 in FIG. 1.

In this example, biometric device 904 is coupled, attached, or imbeddedin a handle of shopping container 900. However, biometric device 904 maybe coupled, attached, or imbedded in or on any part or member ofshopping container 900.

In another embodiment, biometric device 904 is coupled, attached,associated with, or imbedded within display device 902. In this example,display device 902 may use biometric device 904 to dynamicallyidentifying the customer by scanning the customer's fingerprint and/oranalyzing data associated with the customer's fingerprint to determinethe customer's identity.

FIG. 10 is a block diagram of a shelf in a retail facility in accordancewith an illustrative embodiment. Shelf 1000 is any type of device forshowing, displaying, storing, or holding items. Shelf 1000 may be ashelf in a refrigerator or a freezer, as well as a shelf at roomtemperature. Shelf 1000 includes biometric sensors 1002-1008 fordetecting biometric data associated with a customer. When a customer isstanding in proximity to shelf 1000, such as when a customer isshopping, browsing, and/or selecting one or more items for purchase,biometric sensors 1002-1008 monitor biometric readings associated withthe customer, such as, without limitation, the customer's heart rate,respiration rate, body temperature, pupil dilation, fingerprint,thumbprint, and/or any other biometric data. The customer's positive andnegative reactions to customized marketing messages and/or items offeredfor sale are determined by analyzing the biometric data gathered bybiometric sensors 1002-1008.

FIG. 11 is a block diagram illustrating a set of risk assessment factorsused to generate a risk assessment score for a customer in accordancewith an illustrative embodiment. Risk assessment factors are factorsthat are used to generate total risk assessment score 1124 for acustomer. The risk assessment factors are used to determine thepotential risk a customer poses to the retail facility. Risk assessmentfactors includes factors such as, but not limited to, a customer'scredit score 1102, amount of revenue per transaction 1104 generated bythe customer, coupons/discounts/price matching 1106 and other indicatorsthat a customer is cost-conscious, sale items purchased pertransaction/price sensitivity 1108 of the customer, name brands versusgeneric brands 1110 purchased by the customer, shoplifting/criminalhistory 1112, customer history/customer loyalty to the retail facility1114, customer income 1116, frequency of transactions/regularity ofpatronage 1118, product returns 1120, and/or customer complaints 1122made by the customer. Risk assessment factors could include all theserisk factors or only some of these risk factors.

Risk assessment factors could also include additional factors not shownin FIG. 11, such as number of items returned, number of service callsmade, number of items exchanged, number of children brought into theretail facility during shopping trips, number of civil lawsuits filedagainst retail facilities, history of frivolous lawsuits filed againstbusinesses, liens against the customer's property, a history of lawsuitsagainst the retail facility that were settled out of court, and anyother factors that could indicate whether a customer is a desirablecustomer or a customer that poses a potential risk or threat to thestore.

FIG. 12 is a block diagram illustrating a risk assessment engine forgenerating a risk assessment score for a customer in accordance with anillustrative embodiment. Risk assessment engine 1200 is a riskassessment engine for identifying risk assessment factors and generatingrisk assessment scores for customers, such as risk assessment engine 421in FIG. 4.

Risk assessment engine 1200 generates risk assessment factors 1202 basedon a customer profile, such as profile data 406 in FIG. 4, credithistory and credit rating, bankruptcy filings, civil and criminallawsuits, data regarding the customer's past purchases, exchanges, andreturns, criminal records, court records, and other publicly availableinformation regarding the customer.

Risk assessment engine 1200 processes risk assessment factors 1202 inderived model 1204 to generate weighted risk assessment factors 1206.Derived model 1204 processes risk assessment factors 1202 using at leastone of a statistical method, a data mining method, and/or pre-generatedmanual input from users to generate weighted risk assessment factors1206. Weighted risk assessment factors 1206 take into account the factthat some risk factors are more important than others. For example, if acustomer has a history of shoplifting, this factor is of more importancethan a risk factor that indicates the customer makes frequent customercomplaints.

Weighted risk assessment factors 1206 are processed with cohort data1208 to generate weighted risk assessment score 1210. Cohort data 1208is data describing the customer, such as the customer's appearance andbehavior. The cohort data may describe the customer as wearing a trenchcoat in warm weather or wearing sunglasses indoors. Cohort data 1208 mayalso include data describing behavior of the customer, such as, withoutlimitation, walking fast, walking slowly, carrying a large bag,loitering, pacing, or any other behaviors and/or behavior patterns ofthe customer. Cohort data 1208 may also include profile data for thecustomer, such as, profile data 406 in FIG. 4.

Turning now to FIG. 13, a flowchart illustrating a process formonitoring for a change in biometric readings associated with a customeris depicted in accordance with an illustrative embodiment. The processmay be implemented by a device for measuring physiological responsesand/or traits of a customer, such as biometric devices 218 in FIG. 2and/or biometric device 904 in FIG. 9.

The process begins by monitoring biometric readings of a customerobtained from a set of one or more biometric devices (step 1302). Theprocess makes a determination as to whether a change in the biometricreadings that exceeds a threshold change has been detected (step 1304).If a change exceeding the threshold is not detected, the processterminates thereafter.

Returning to step 1304, if a change exceeding the threshold is detected,the process makes a determination as to whether the customer was viewingan item, a marketing message, or some other identifiable person, place,or thing when the change in biometric readings occurred (step 1306). Ifthe customer was not viewing an item, a marketing message, or some otheridentifiable person, place, or thing, the process terminates thereafter.

Returning to step 1306, if the customer was viewing an item, marketingmessage, or something else identifiable, the process associates thechange in biometric reading with the item, the marketing message, or theidentifiable person, place, or thing to form the biometric data (step1308). The process transmits the biometric data to an analysis serverand/or stores the biometric data in a data storage device for later usein generating customized marketing messages in the future (step 1310)with the process terminating thereafter.

FIG. 14 is a flowchart illustrating a process for generating dynamicdata for a customer in accordance with an illustrative embodiment. Theprocess is implemented by smart detection system 1000 in FIG. 10. Theprocess begins by receiving data for a customer from a set of detectorsassociated with the retail facility (step 1402). The data may be,without limitation, audio and/or video data from a camera located eitherinside or outside the retail facility. The process analyzes the data toform dynamic data for the customer (step 1404). The analysis involvesusing behavior analysis, license plate recognition, facial recognition,badge reader, radar analytics, and other analysis on the data. Theprocess sends the dynamic data to an analysis server and/or stores thedynamic data in a data storage device (step 1406) with the processterminating thereafter.

FIG. 15 is a flowchart illustrating a process for identifying anundesirable customer in accordance with an illustrative embodiment. Theprocess is implemented by risk assessment engine 421 in FIG. 4. Theprocess begins by identifying a customer associated with a retailfacility (step 1502). The customer may be outside the retail facility,such as, without limitation, in a parking lot, or inside the retailfacility. The process retrieves dynamic data for the customer (step1506). The process retrieves any biometric data for the customer (step1510). The process performs a risk assessment analysis using theavailable dynamic data and/or biometric data (step 1512). The processalso uses static customer data elements from a customer profile. Therisk assessment analysis identifies risk assessment factors using thedynamic data, biometric data, and/or static customer data elements.

The process generates a risk assessment score for the customer based onthe results of the risk assessment analysis (step 1514). The processmakes a determination as to whether the risk assessment score indicatesthe customer is a desirable customer (step 1516). If the customer is adesirable customer, the process generates marketing incentives targetedto the customer (step 1518) with the process terminating thereafter. Ifthe customer is not a desirable customer, the process generatesmarketing disincentives (step 1520) with the process terminatingthereafter.

FIG. 16 is a flowchart illustrating a process for generating a riskassessment score in accordance with an illustrative embodiment. Theprocess is implemented by risk assessment engine 421 in FIG. 4. Theprocess begins by identifying risk assessment factors (step 1602) forthe customer. The process analyzes the risk assessment factors usingdata mining, statistical methods, predefined weighting guidelines,and/or pre-generated manual input from users (step 1604). The processgenerates the weighted risk factors (step 1606) and analyzes theweighted risk factors with cohort data for the customer (step 1608). Theprocess then generates a weighted risk assessment score for the customerusing the weighted risk assessment factors and the cohort data (step1610) with the process terminating thereafter.

FIG. 17 is a flowchart illustrating a process for updating a riskassessment score in accordance with an illustrative embodiment. Theprocess is implemented by risk assessment engine 421 in FIG. 4. Theprocess begins by making a determination as to whether a risk assessmentscore is available for the customer (step 1702). If a risk assessmentscore is available, the process retrieves the risk assessment score froma customer profile (step 1704). The process determines if the customeris a desirable customer or an undesirable customer based on the riskassessment score (step 1706).

If the score is not available at step 1702, the process generates a riskassessment score using dynamic data for the customer and/or biometricdata (step 1710). The process stores the risk assessment score in acustomer profile for the customer (step 1712).

The process makes a determination as to whether new dynamic data and/ornew biometric data for the customer is available (step 1708). If newdynamic data and/or biometric data is not available, the processterminates thereafter. If new data is available, the process performs arisk assessment analysis on the new dynamic data and/or biometric data(step 1714). The process updates the risk assessment score using theresults of the risk assessment analysis (step 1716) with the processterminating thereafter.

FIG. 18 is a flowchart illustrating a process for preferred customermarketing in accordance with an illustrative embodiment. The process isimplemented by risk assessment engine 421 in FIG. 4. The process beginsby analyzing a risk assessment score (step 1802) for a customer. Theprocess makes a determination as to whether the risk assessment score isgreater than an upper threshold score (step 1804). If the score isgreater than the upper threshold, the process identifies the customer asan undesirable customer (step 1806) and initiates marketingdisincentives (step 1808) directed towards the customer with the processterminating thereafter.

Returning to step 1804, if the score is not greater than the upperthreshold, the process makes a determination as to whether the riskassessment score is lower than a lower threshold score (step 1810). Ifthe score is lower than the lower threshold, the process identifies thecustomer as a highly desirable customer (step 1812) and initiatesaggressive marketing incentives targeted to the customer (step 1814)with the process terminating thereafter.

Returning to step 1810, if the risk assessment score is lower than theupper threshold and higher than the lower threshold, the processidentifies the customer as a moderately desirable/average customer (step1816). The process initiates moderate marketing incentives targeted tothe customer (step 1818) with the process terminating thereafter.

In another embodiment, a customer is identified as an undesirablecustomer is the risk assessment score is lower than a lower threshold.In this case, a customer is identified as a desirable customer if therisk assessment score is greater than an upper threshold. In otherwords, any type of scoring method and threshold may be used to identifycustomers that are more likely to cause a risk of financial lossesand/or legal problems for the retail facility than other customers.

FIG. 19 is a flowchart illustrating a process for marketingdisincentives in accordance with an illustrative embodiment. The processis implemented by risk assessment engine 421 in FIG. 4. The processbegins by making a determination as to whether to initiate marketingdisincentives targeted to the customer (step 1902). If marketingdisincentives are initiated, the process generates customized marketingmessages for the customer that includes undesirable or uncompetitiveproduct pricing and product offers (step 1904) and directs salesassociates/retail store employees to limit customer assistance orprovide no customer assistance to the customer (step 1906) with theprocess terminating thereafter.

If marketing disincentives are not initiated, the process generatescustomized marketing messages including competitive or desirable productpricing and product offers (step 1908) and directs sales associates tofocus customer assistance efforts towards the customer (step 1910) withthe process terminating thereafter.

FIG. 20 is a flowchart illustrating a process for generating acustomized marketing message using dynamic data in accordance with anillustrative embodiment. The process in FIG. 20 is implemented by aserver, such as analysis server 402 in FIG. 4.

The process begins by retrieving any available dynamic data for acustomer (step 2004). The dynamic data includes, without limitation,grouping data, external data, customer identification data, vehicleidentification data, customer behavior data, and/or any other dynamiccustomer data elements. The process retrieves any available biometricdata (step 2006) for the customer.

The process pre-generates or creates in advance, appropriate data modelsusing at least one of a statistical method, data mining method, causalmodel, mathematical model, marketing model, behavioral model,psychographical model, sociological model, simulations/modelingtechniques, and/or any combination of models, data mining, statisticalmethods, simulations and/or modeling techniques (step

The process analyzes dynamic data using one or more of the appropriatedata models to identify a set of personalized marketing message criteria(step 2010). The set of personalized marketing message criteria mayinclude one or more criterion for generating a personalized marketingmessage.

The process makes a determination as to whether to initiate marketingdisincentives directed towards the customer (step 2012). If marketingdisincentives are not initiated, the process dynamically builds a set ofone or more customized marketing messages using the personalizedmarketing message criteria and marketing incentives (step 2014). Ifmarketing disincentives are initiated, the process dynamically builds aset of one or more customized marketing messages using the personalizedmarketing message criteria and marketing disincentives (step 2015). Theprocess then transmits the set of customized marketing messages to adisplay device associated with the customer (step 2016) for presentationof the marketing message to the customer, with the process terminatingthereafter.

Thus, the illustrative embodiments provide a computer implementedmethod, apparatus, and computer program product for generating acustomer risk assessment score. In one embodiment, the process parsesdynamic data associated with a customer to identify patterns of events.The dynamic data comprises metadata describing an appearance andbehavior of the customer. The process analyzes the patterns of events toidentify risk assessment factors for the customer. The process performsa risk assessment analysis using the risk assessment factors for thecustomer to generate a risk assessment score for the customer, whereinthe risk assessment score indicates a potential risk posed by thecustomer to the retail facility.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus, methods and computer programproducts. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of computer usable orreadable program code, which comprises one or more executableinstructions for implementing the specified function or functions. Insome alternative implementations, the function or functions noted in theblock may occur out of the order noted in the figures. For example, insome cases, two blocks shown in succession may be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

Further, a computer storage medium may contain or store a computerreadable program code such that when the computer readable program codeis executed on a computer, the execution of this computer readableprogram code causes the computer to transmit another computer readableprogram code over a communications link. This communications link mayuse a medium that is, for example without limitation, physical orwireless.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer implemented method for generating a customer riskassessment score, the computer implemented method comprising: parsingdynamic data associated with a customer to identify patterns of events,wherein the dynamic data comprises metadata describing an appearance andbehavior of the customer; analyzing the patterns of events to identifyrisk assessment factors for the customer; dynamically performing a riskassessment analysis using the risk assessment factors for the customerto generate a risk assessment score for the customer while the customeris shopping in a retail facility, wherein the risk assessment scoreindicates a potential risk posed by the customer to the retail facility.2. The computer implemented method of claim 1 further comprising:receiving data for the customer from a set of biometric sensorsassociated with the retail facility to form biometric data; analyzingthe biometric data with the dynamic data to identify the risk assessmentfactors for the customer.
 3. The computer implemented method of claim 2wherein analyzing the biometric data further comprising: detecting achange in a biometric reading associated with the customer that exceedsa threshold change to form the biometric data; responsive to adetermination that the customer was viewing an item or a marketingmessage when the change in the biometric reading occurred, associatingthe change in the biometric reading with the item or the marketingmessage to form the biometric data; and responsive to a determinationthat the customer was interacting with another customer, an employee ofthe retail facility, a child, or an animal, associating the change inthe biometric reading with the another customer, the employee, thechild, or the animal.
 4. The computer implemented method of claim 1wherein the potential threat posed by the customer to the retailfacility includes at least one of shoplifting, theft, violence, failureto pay bills, defaulting on loans, disrupting operations, criminalactivities, threatening customers, panhandling, and loitering.
 5. Thecomputer implemented method of claim 1 further comprising: receivingdata from a set of detectors associated with the retail facility to formdetection data; and analyzing the detection data using a set of datamodels, by a smart detection engine, to form the dynamic data, whereinthe dynamic data comprises metadata describing the customer.
 6. Thecomputer implemented method of claim 2 wherein the set of detectorscomprises a set of cameras and wherein the detection data is derivedfrom a continuous stream of video data captured by the set of cameras.7. The computer implemented method of claim 1 further comprising:retrieving a customer profile for the customer, wherein the customerprofile comprises static customer data elements describing the customer;and analyzing the dynamic data with the customer profile to identify therisk assessment factors.
 8. The computer implemented method of claim 1wherein parsing the event data further comprises: processing the eventdata using at least one of a statistical method, a data mining method, acausal model, a mathematical model, a marketing model, a behavioralmodel, a psychological model, a sociological model, or a simulationmodel.
 9. The computer implemented method of claim 1 wherein performingthe risk assessment analysis further comprises: analyzing the riskassessment factors using at least one of a statistical method, a datamining method, and pre-generated manual input to generate weighted riskfactors; and generating the risk assessment score using the weightedrisk assessment factors and cohort data for the customer, wherein cohortdata comprises data describing the customer.
 10. The computerimplemented method of claim 1 wherein the dynamic data comprisesgrouping data for the customer, wherein the grouping data describes agroup associated with the customer and further comprising: generating arisk assessment score for each member of the group associated with thecustomer.
 11. The computer implemented method of claim 10 wherein thegroup associated with the customer is a group consisting of parents withchildren, teenagers, children, minors unaccompanied by adults, minorsaccompanied by adults, grandparents with grandchildren, senior citizens,couples, friends, coworkers, a customer shopping with a pet, and acustomer shopping alone
 12. The computer implemented method of claim 1further comprising: automatically analyzing the dynamic data to generatecustomer identification data, wherein the customer identification dataidentifies the customer without human input, wherein automaticallyanalyzing the dynamic data comprises analyzing the dynamic data using atleast one of facial recognition analysis on an image of a face of thecustomer, license plate recognition analysis on an image of a vehiclelicense plate, a fingerprint analysis on a fingerprint of the customer,and voice analysis on a sound file.
 13. The computer implemented methodof claim 1 further comprising: receiving external marketing data from aset of sources to form the current events data; and processing thecurrent events data to form the dynamic data, wherein analyzing thepatterns of events to identify the risk assessment factors for thecustomer further comprises identifying events in the current events dataindicating a potential risk to the retail facility associated with thecustomer.
 14. A computer program product comprising: a computer usablemedium including computer usable program code for generating a customerrisk assessment score, said computer program product comprising:computer usable program code for parsing dynamic data associated with acustomer to identify patterns of events, wherein the dynamic datacomprises metadata describing an appearance and behavior of thecustomer; computer usable program code for analyzing the patterns ofevents to identify risk assessment factors for the customer; andcomputer usable program code for dynamically performing a riskassessment analysis using the risk assessment factors for the customerto generate a risk assessment score for the customer while the customeris shopping in a retail facility, wherein the risk assessment scoreindicates a potential risk posed by the customer to the retail facility.15. The computer program product of claim 14 further comprising:computer usable program code for receiving data for the customer from aset of biometric sensors associated with the retail facility to formbiometric data; computer usable program code for analyzing the biometricdata with the dynamic data to identify the risk assessment factors forthe customer, wherein analyzing the biometric data further comprising:computer usable program code for detecting a change in a biometricreading associated with the customer that exceeds a threshold change toform the biometric data; computer usable program code for responsive toa determination that the customer was viewing an item or a marketingmessage when the change in the biometric reading occurred, associatingthe change in the biometric reading with the item or the marketingmessage to form the biometric data; and computer usable program code forresponsive to a determination that the customer was interacting withanother customer, an employee of the retail facility, a child, or ananimal, associating the change in the biometric reading with the anothercustomer, the employee, the child, or the animal.
 16. The computerprogram product of claim 14 wherein the potential threat posed by thecustomer to the retail facility includes at least one of shoplifting,theft, violence, failure to pay bills, defaulting on loans, disruptingoperations, criminal activities, threatening customers, panhandling, andloitering.
 17. The computer program product of claim 14 furthercomprising: computer usable program code for receiving data from a setof detectors associated with the retail facility to form detection data;and computer usable program code for analyzing the detection data usinga set of data models, by a smart detection engine, to form the dynamicdata, wherein the dynamic data comprises metadata describing thecustomer.
 18. The computer program product of claim 17 wherein the setof detectors comprises a set of cameras and wherein the detection datais derived from a continuous stream of video data captured by the set ofcameras.
 19. The computer program product of claim 14 furthercomprising: computer usable program code for retrieving a customerprofile for the customer, wherein the customer profile comprises staticcustomer data elements describing the customer; and computer usableprogram code for analyzing the dynamic data with the customer profile toidentify the risk assessment factors.
 20. The computer program productof claim 14 wherein performing the risk assessment analysis furthercomprises: computer usable program code for analyzing the riskassessment factors using at least one of a statistical method, a datamining method, and pre-generated manual input to generate weighted riskfactors; and computer usable program code for generating the riskassessment score using the weighted risk assessment factors and cohortdata for the customer, wherein cohort data comprises data describing thecustomer.
 21. A data processing system for generating a customer riskassessment score, the data processing system comprising: a bus system; acommunications system connected to the bus system; a memory connected tothe bus system, wherein the memory includes computer usable programcode; and a processing unit connected to the bus system, wherein theprocessing unit executes the computer usable program code to parsedynamic data associated with a customer to identify patterns of events,wherein the dynamic data comprises metadata describing an appearance andbehavior of the customer; analyze the patterns of events to identifyrisk assessment factors for the customer; dynamically perform a riskassessment analysis using the risk assessment factors for the customerto generate a risk assessment score for the customer while the customeris shopping in a retail facility, wherein the risk assessment scoreindicates a potential risk posed by the customer to the retail facility.22. The data processing system of claim 21 wherein the potential threatposed by the customer to the retail facility includes at least one ofshoplifting, theft, violence, failure to pay bills, defaulting on loans,disrupting operations, criminal activities, threatening customers,panhandling, and loitering.
 23. A system for generating a customer riskassessment score, the system comprising: an analysis server, wherein theanalysis server parses dynamic data associated with a customer toidentify patterns of events and analyze the patterns of events toidentify risk assessment factors for the customer; and a risk assessmentengine, wherein the risk assessment engine dynamically performs a riskassessment analysis using the risk assessment factors for the customerto generate a risk assessment score for the customer while the customeris shopping in a retail facility, wherein the risk assessment scoreindicates a potential risk posed by the customer to the retail facility.24. The system of claim 23 further comprising: a set of biometricsensors associated with the retail facility, wherein the set ofbiometric sensors captures biometric data associated with the customer,and wherein the risk assessment engine analyzes the biometric data withthe dynamic data to identify the risk assessment factors for thecustomer.
 25. The system of claim 23 further comprising: a set ofdetectors associated with the retail facility, wherein the set ofdetectors captures data associated with the customer to form detectiondata; and a smart detection engine, wherein the smart detection engineanalyzes the detection data using a set of data models to form thedynamic data, wherein the dynamic data comprises metadata describing anappearance of the customer and behavior of the customer.